Client-Side Black-Box Monitoring for Web Sites



In spite of their growing maturity, current web
monitoring tools are unable to observe all operating conditions.
For example, clients in different geographical locations might
get very diverse latencies to the server; the network between
client and server might be slow; or third-party servers with
external page resources might underperform. Ultimately, only
the clients can determine whether a site is up and running in
good conditions.
In this paper, we use the response times experienced by
clients, to infer about server and network performance. The goal
is to detect internal and external bottlenecks doing black-box
monitoring, in particular CPU (internal) and network (external).
We aim to determine to what extent are the clients able to
tell one type of bottleneck from the other, i.e., what kind of
information do the server and network leak, regarding their
operating conditions.
To answer this question, we resort to an empirical approach.
We submit an HTTP server and network to a large number of
operating conditions and train two machine learning algorithms,
a linear and a non-linear one, to identify the cause of the
congestion affecting the system. Results show that the server and
network leak information to a level of detail that allows sorting
out CPU from network bottlenecks, or even a combination of the
two, in a large spectrum of cases. This suggests that a black-box
monitoring approach is not only possible, but promising, as it
may complement traditional white-box approaches.

Related Project

DataScience4NP: Data Science for Non-Programmers


The 16th IEEE International Symposium on Network Computing and Applications (NCA 2017), October 2017

PDF File

Cited by

No citations found